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Funder: Biometrika Trust, Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not necessarily pre-specified. Online error rate control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time. In the online multiple hypothesis testing framework, hypotheses are tested one-by-one over time, where at each time-step an analyst decides whether to reject the current null hypothesis without knowledge of future tests but based solely on past decisions. Methodology has recently been developed for online control of the false discovery rate as well as the familywise error rate (FWER). In this article, we describe how to apply online error rate control to the platform trial setting, present extensive simulation results, and give some recommendations for the use of this new methodology in practice. We show that the algorithms for online error rate control can have a substantially lower FWER than uncorrected testing, while still achieving noticeable gains in power when compared with the use of a Bonferroni correction. We also illustrate how online error rate control would have impacted a currently ongoing platform trial.

BACKGROUND: Sequential B cell-targeted immunotherapy with BAFF antagonism (belimumab) and B cell depletion (rituximab) may enhance B cell targeting in ANCA-associated vasculitis (AAV) through several mechanisms. METHODS: Study design: COMBIVAS is a randomised, double-blind, placebo-controlled trial designed to assess the mechanistic effects of sequential therapy of belimumab and rituximab in patients with active PR3 AAV. The recruitment target is 30 patients who meet the criteria for inclusion in the per-protocol analysis. Thirty-six participants have been randomised to one of the two treatment groups in a 1:1 ratio: either rituximab plus belimumab or rituximab plus placebo (both groups with the same tapering corticosteroid regimen), and recruitment is now closed (final patient enrolled April 2021). For each patient, the trial will last for 2 years comprising a 12-month treatment period followed by a 12-month follow-up period. PARTICIPANTS: Participants have been recruited from five of seven UK trial sites. Eligibility criteria were age ≥ 18 years and a diagnosis of AAV with active disease (newly diagnosed or relapsing disease), along with a concurrent positive test for PR3 ANCA by ELISA. INTERVENTIONS: Rituximab 1000 mg was administered by intravenous infusions on day 8 and day 22. Weekly subcutaneous injections of 200 mg belimumab or placebo were initiated a week before rituximab on day 1 and then weekly through to week 51. All participants received a relatively low prednisolone (20 mg/day) starting dose from day 1 followed by a protocol-specified corticosteroid taper aiming for complete cessation by 3 months. OUTCOMES: The primary endpoint of this study is time to PR3 ANCA negativity. Key secondary outcomes include change from baseline in naïve, transitional, memory, plasmablast B cell subsets (by flow cytometry) in the blood at months 3, 12, 18 and 24; time to clinical remission; time to relapse; and incidence of serious adverse events. Exploratory biomarker assessments include assessment of B cell receptor clonality, B cell and T cell functional assays, whole blood transcriptomic analysis and urinary lymphocyte and proteomic analysis. Inguinal lymph node and nasal mucosal biopsies have been performed on a subgroup of patients at baseline and month 3. DISCUSSION: This experimental medicine study provides a unique opportunity to gain detailed insights into the immunological mechanisms of belimumab-rituximab sequential therapy across multiple body compartments in the setting of AAV. TRIAL REGISTRATION: ClinicalTrials.gov NCT03967925. Registered on May 30, 2019.

Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely.

Funder: RCUK | MRC | Medical Research Foundation; doi: https://doi.org/10.13039/501100009187, Funder: NIHR Biomedical Research Centre BRC-1215-20014, BACKGROUND: Efficient trial designs are required to prioritise promising drugs within Phase II trials. Adaptive designs are examples of such designs, but their efficiency is reduced if there is a delay in assessing patient responses to treatment. METHODS: Motivated by the WIRE trial in renal cell carcinoma (NCT03741426), we compare three trial approaches to testing multiple treatment arms: (1) single-arm trials in sequence with interim analyses; (2) a parallel multi-arm multi-stage trial and (3) the design used in WIRE, which we call the Multi-Arm Sequential Trial with Efficient Recruitment (MASTER) design. The MASTER design recruits patients to one arm at a time, pausing recruitment to an arm when it has recruited the required number for an interim analysis. We conduct a simulation study to compare how long the three different trial designs take to evaluate a number of new treatment arms. RESULTS: The parallel multi-arm multi-stage and the MASTER design are much more efficient than separate trials. The MASTER design provides extra efficiency when there is endpoint delay, or recruitment is very quick. CONCLUSIONS: We recommend the MASTER design as an efficient way of testing multiple promising cancer treatments in non-comparative Phase II trials.

Phase II immuno-oncology clinical trials screen for efficacy an increasing number of treatments. In rare cancers, using historical control data is a pragmatic approach for speeding up clinical trials. The drop-the-losers design allows dropping off ineffective arms at interim analyses. We extended the original drop-the-losers design for a time-to-event outcome using a historical control through the one-sample log-rank statistic. Simulated trials featured three arms at the first stage, one at the second stage, nine scenarios, eight sample sizes with 5%- and 10%- nominal family-wise error rate (FWER). A numerical algorithm is provided to solve power calculations at the design stage. Our design was compared with a group of three independent single-arm trials (fixed design) with and without correction for multiplicity. Our design allowed strict control of the FWER at nominal levels while the misspecification of survival distribution and fixed design inflated the FWER up to three times the nominal level. The empirical power of our design increased with the sample size, the treatment effect and the number of effective treatments and dropped when more patients were recruited at the second stage. The fixed design with correction showed comparable power, while our design advantageously included more patients to the most promising arm. Recommendations for future applications are given. By taking advantage of the use of historical control data and a time-to-event outcome, the drop-the-losers design is a promising tool to meet the challenge of improving phase II clinical trials in immuno-oncology.

Purpose To evaluate glioblastoma (GBM) metabolism by using hyperpolarized carbon 13 (13C) MRI to monitor the exchange of the hyperpolarized 13C label between injected [1-13C]pyruvate and tumor lactate and bicarbonate. Materials and Methods In this prospective study, seven treatment-naive patients (age [mean ± SD], 60 years ± 11; five men) with GBM were imaged at 3 T by using a dual-tuned 13C-hydrogen 1 head coil. Hyperpolarized [1-13C]pyruvate was injected, and signal was acquired by using a dynamic MRI spiral sequence. Metabolism was assessed within the tumor, in the normal-appearing brain parenchyma (NABP), and in healthy volunteers by using paired or unpaired t tests and a Wilcoxon signed rank test. The Spearman ρ correlation coefficient was used to correlate metabolite labeling with lactate dehydrogenase A (LDH-A) expression and some immunohistochemical markers. The Benjamini-Hochberg procedure was used to correct for multiple comparisons. Results The bicarbonate-to-pyruvate (BP) ratio was lower in the tumor than in the contralateral NABP (P < .01). The tumor lactate-to-pyruvate (LP) ratio was not different from that in the NABP (P = .38). The LP and BP ratios in the NABP were higher than those observed previously in healthy volunteers (P < .05). Tumor lactate and bicarbonate signal intensities were strongly correlated with the pyruvate signal intensity (ρ = 0.92, P < .001, and ρ = 0.66, P < .001, respectively), and the LP ratio was weakly correlated with LDH-A expression in biopsy samples (ρ = 0.43, P = .04). Conclusion Hyperpolarized 13C MRI demonstrated variation in lactate labeling in GBM, both within and between tumors. In contrast, bicarbonate labeling was consistently lower in tumors than in the surrounding NABP. Keywords: Hyperpolarized 13C MRI, Glioblastoma, Metabolism, Cancer, MRI, Neuro-oncology Supplemental material is available for this article. Published under a CC BY 4.0 license.

Umbrella trials are an innovative trial design where different treatments are matched with subtypes of a disease, with the matching typically based on a set of biomarkers. Consequently, when patients can be positive for more than one biomarker, they may be eligible for multiple treatment arms. In practice, different approaches could be applied to allocate patients who are positive for multiple biomarkers to treatments. However, to date there has been little exploration of how these approaches compare statistically. We conduct a simulation study to compare five approaches to handling treatment allocation in the presence of multiple biomarkers - equal randomisation; randomisation with fixed probability of allocation to control; Bayesian adaptive randomisation (BAR); constrained randomisation; and hierarchy of biomarkers. We evaluate these approaches under different scenarios in the context of a hypothetical phase II biomarker-guided umbrella trial. We define the pairings representing the pre-trial expectations on efficacy as linked pairs, and the other biomarker-treatment pairings as unlinked. The hierarchy and BAR approaches have the highest power to detect a treatment-biomarker linked interaction. However, the hierarchy procedure performs poorly if the pre-specified treatment-biomarker pairings are incorrect. The BAR method allocates a higher proportion of patients who are positive for multiple biomarkers to promising treatments when an unlinked interaction is present. In most scenarios, the constrained randomisation approach best balances allocation to all treatment arms. Pre-specification of an approach to deal with treatment allocation in the presence of multiple biomarkers is important, especially when overlapping subgroups are likely.

IMPORTANCE: Multiple analyses in a clinical trial can increase the probability of inaccurately concluding that there is a statistically significant treatment effect. However, to date, it is unknown how many randomized clinical trials (RCTs) perform adjustments for multiple comparisons, the lack of which could lead to erroneous findings. OBJECTIVES: To assess the prevalence of multiplicity and whether appropriate multiplicity adjustments were performed among cardiovascular RCTs published in 6 medical journals with a high impact factor. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, cardiovascular RCTs were selected from all over the world, characterized as North America, Western Europe, multiregional, and rest of the world. Data were collected from past issues of 3 cardiovascular journals (Circulation, European Heart Journal, and Journal of the American College of Cardiology) and 3 general medicine journals (JAMA, The Lancet, and The New England Journal of Medicine) with high impact factors published between August 1, 2015, and July 31, 2018. Supplements and trial protocols of each of the included RCTs were also searched for multiplicity. Data were analyzed December 20 to 27, 2018. EXPOSURES: Data from the selected RCTs were extracted and verified independently by 2 researchers using a structured data instrument. In case of disagreement, a third reviewer helped to achieve consensus. An RCT was considered to have multiple treatment groups if it had more than 2 arms; multiple outcomes were defined as having more than 1 primary outcome, and multiple analyses were defined as analysis of the same outcome variable in multiple ways. Multiplicity was examined only for the analysis of the primary end point. MAIN OUTCOMES AND MEASURES: Outcomes of interest were percentages of primary analyses that performed multiplicity adjustment of primary end points. RESULTS: Of 511 cardiovascular RCTs included in this analysis, 300 (58.7%) had some form of multiplicity; of these 300, only 85 (28.3%) adjusted for multiplicity. Intervention type and funding source had no statistically significant association with the reporting of multiplicity risk adjustment. Trials that assessed mortality vs nonmortality outcomes were more likely to contain a multiplicity risk in their primary analysis (66.3% [177 of 267] vs 50.4% [123 of 244]; P < .001), and larger trials vs smaller trials were less likely to make any adjustments for multiplicity (35.6% [52 of 146] vs 21.4% [33 of 154]; P = .001). CONCLUSIONS AND RELEVANCE: Findings from this study suggest that cardiovascular RCTs published in medical journals with high impact factors demonstrate infrequent adjustments to correct for multiple comparisons in the primary end point. These parameters may be improved by more standardized reporting.

BACKGROUND: Multisystemic therapy is a manualised treatment programme for young people aged 11-17 years who exhibit antisocial behaviour. To our knowledge, the Systemic Therapy for At Risk Teens (START) trial is the first large-scale randomised controlled trial of multisystemic therapy in the UK. Previous findings reported to 18 months after baseline (START-I study) did not indicate superiority of multisystemic therapy compared with management as usual. Here, we report outcomes of the trial to 60 months (START-II study). METHODS: In this pragmatic, randomised, controlled, superiority trial, young people (aged 11-17 years) with moderate-to-severe antisocial behaviour were recruited from social services, youth offending teams, schools, child and adolescent mental health services, and voluntary services across England, UK. Participants were eligible if they had at least three severity criteria indicating past difficulties across several settings and one of five general inclusion criteria for antisocial behaviour. Eligible families were randomly assigned (1:1), using stochastic minimisation and stratifying for treatment centre, sex, age at enrolment, and age at onset of antisocial behaviour, to management as usual or 3-5 months of multisystemic therapy followed by management as usual. Research assistants and investigators were masked to treatment allocation; the participants could not be masked. For this extension study, the primary outcome was the proportion of participants with offences with convictions in each group at 60 months after randomisation. This study is registered with ISRCTN, ISRCTN77132214, and is closed to accrual. FINDINGS: Between Feb 4, 2010, and Sept 1, 2012, 1076 young people and families were assessed for eligibility and 684 were randomly assigned to management as usual (n=342) or multisystemic therapy (n=342). By 60 months' of follow-up, 188 (55%) of 342 people in the multisystemic therapy group had at least one offence with a criminal conviction, compared with 180 (53%) of 341 in the management-as-usual group (odds ratio 1·13, 95% CI 0·82-1·56; p=0·44). INTERPRETATION: The results of the 5-year follow-up show no evidence of longer-term superiority for multisystemic therapy compared with management as usual. FUNDING: National Institute for Health Research Health Services and Delivery Research programme.

INTRODUCTION: The value of antineutrophil cytoplasmic antibody (ANCA) measurements among patients with an established diagnosis of ANCA-associated vasculitis (AAV) to assess disease activity or predict relapse remains controversial, but recent evidence suggests a possible role for rituximab-treated patients. PATIENTS AND METHODS: All patients with active vasculitis and positive proteinase 3 (PR3)-ANCA who were starting a 2-year treatment course of rituximab for induction of remission at Addenbrooke's Hospital between January 2011 and January 2016 were included in this study. Common department practice consists of 6 g of rituximab given over 2 years, concomitant corticosteroids (0.5-1.0 mg/kg) with rapid taper over 3 months, and cessation of oral maintenance immunosuppressive agents at time of first rituximab dose. Clinical and laboratory data were collected retrospectively using electronic patient records. RESULTS: Fifty-seven patients with current PR3-ANCA positivity were included in the analysis. Median follow-up was 59 months. PR3-ANCA negativity was achieved in 25 patients (44%) with a median time of 14 months. Clinical remission was achieved in 53 patients (93%) with a median time of 3 months. Among the 53 patients who achieved remission during follow-up, 24 (45%) relapsed with a median time to relapse of 36 months from remission. Both PR3-ANCA-negative status and 50% reduction in PR3-ANCA from baseline (as time-varying covariates) were significantly associated with a longer time to relapse (PR3-ANCA-negative status: hazards ratio, 0.08 [95% confidence interval, 0.01-0.63, p = 0.016]; 50% reduction in PR3-ANCA: hazards ratio, 0.25 [95% confidence interval, 0.18-0.99, p = 0.046]). CONCLUSIONS: Achieving and maintaining PR3-ANCA negativity after rituximab was associated with longer-lasting remission.

OBJECTIVE: To provide guidance to researchers, funders, regulators and study delivery teams to ensure that research on COVID-19 is inclusive, particularly of groups disproportionately affected by COVID-19 and who may have been historically under-served by research. SUMMARY OF KEY POINTS: Groups who are disproportionately affected by COVID-19 include (but are not limited to) older people, people with multiple long-term conditions, people with disabilities, people from Black, Asian and Ethnic minority groups, people living with obesity, people who are socioeconomically deprived and people living in care homes. All these groups are under-served by clinical research, and there is an urgent need to rectify this if COVID-19 research is to deliver relevant evidence for these groups who are most in need. We provide a framework and checklists for addressing key issues when designing and delivering inclusive COVID-19 research, based on the National Institute for Health Research INnovations in CLinical trial design and delivery for the UnDEr-served project roadmap. Strong community engagement, codevelopment and prioritisation of research questions and interventions are essential. Under-served groups should be represented on funding panels and ethics committees, who should insist on the removal of barriers to participation. Exclusion criteria should be kept to a minimum; intervention delivery and outcome measurement should be simple, flexible and tailored to the needs of different groups, and local advice on the best way to reach and engage with under-served communities should be taken by study delivery teams. Data on characteristics that allow identification of under-served groups must be collected, analyses should include these data to enable subgroup comparisons and results should be shared with under-served groups at an early stage. CONCLUSION: Inclusive COVID-19 research is a necessity, not a luxury, if research is to benefit all the communities it seeks to serve. It requires close engagement with under-served groups and attention to aspects of study topic, design, delivery, analysis and dissemination across the research life cycle.

Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on-going clinical trials using a "platform" trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature. Most works on adding arm(s) assume that a new arm is opened whenever a new treatment becomes available. This strategy may prolong the overall duration of a study or cause reduction in marginal power for each hypothesis if the adaptation is not well accommodated. Within a two-stage trial setting, we propose a decision-theoretic framework to investigate when to add or not to add a new treatment arm based on the observed stage one treatment responses. To account for different prospect of multiarm studies, we define utility in two different ways; one for a trial that aims to maximise the number of rejected hypotheses; the other for a trial that would declare a success when at least one hypothesis is rejected from the study. Our framework shows that it is not always optimal to add a new treatment arm to an existing trial. We illustrate a case study by considering a completed trial on knee osteoarthritis.

Numerous publications have now addressed the principles of designing, analyzing, and reporting the results of stepped-wedge cluster randomized trials. In contrast, there is little research available pertaining to the design and analysis of multiarm stepped-wedge cluster randomized trials, utilized to evaluate the effectiveness of multiple experimental interventions. In this paper, we address this by explaining how the required sample size in these multiarm trials can be ascertained when data are to be analyzed using a linear mixed model. We then go on to describe how the design of such trials can be optimized to balance between minimizing the cost of the trial and minimizing some function of the covariance matrix of the treatment effect estimates. Using a recently commenced trial that will evaluate the effectiveness of sensor monitoring in an occupational therapy rehabilitation program for older persons after hip fracture as an example, we demonstrate that our designs could reduce the number of observations required for a fixed power level by up to 58%. Consequently, when logistical constraints permit the utilization of any one of a range of possible multiarm stepped-wedge cluster randomized trial designs, researchers should consider employing our approach to optimize their trials efficiency.

Background: Multi-arm designs provide an effective means of evaluating several treatments within the same clinical trial. Given the large number of treatments now available for testing in many disease areas, it has been argued that their utilisation should increase. However, for any given clinical trial there are numerous possible multi-arm designs that could be used, and choosing between them can be a difficult task. This task is complicated further by a lack of available easy-to-use software for designing multi-arm trials. Results: To aid the wider implementation of multi-arm clinical trial designs, we have developed a web application for sample size calculation when using a variety of popular multiple comparison corrections. Furthermore, the application supports sample size calculation to control several varieties of power, as well as the determination of optimised arm-wise allocation ratios. It is built using the Shiny package in the R programming language, is free to access on any device with an internet browser, and requires no programming knowledge to use. It incorporates a variety of features to make it easier to use, including help boxes and warning messages. Using design parameters motivated by a recently completed phase II oncology trial, we demonstrate that the application can effectively determine and evaluate complex multi-arm trial designs. Conclusions: The application provides the core information required by statisticians and clinicians to review the operating characteristics of a chosen multi-arm clinical trial design. The range of designs supported by the application is broader than other currently available software solutions. Its primary limitation, particularly from a regulatory agency point of view, is its lack of validation. However, we present an approach to efficiently confirming its results via simulation.

The ability to accurately estimate the sample size required by a stepped-wedge (SW) cluster randomized trial (CRT) routinely depends upon the specification of several nuisance parameters. If these parameters are misspecified, the trial could be overpowered, leading to increased cost, or underpowered, enhancing the likelihood of a false negative. We address this issue here for cross-sectional SW-CRTs, analyzed with a particular linear-mixed model, by proposing methods for blinded and unblinded sample size reestimation (SSRE). First, blinded estimators for the variance parameters of a SW-CRT analyzed using the Hussey and Hughes model are derived. Following this, procedures for blinded and unblinded SSRE after any time period in a SW-CRT are detailed. The performance of these procedures is then examined and contrasted using two example trial design scenarios. We find that if the two key variance parameters were underspecified by 50%, the SSRE procedures were able to increase power over the conventional SW-CRT design by up to 41%, resulting in an empirical power above the desired level. Thus, though there are practical issues to consider, the performance of the procedures means researchers should consider incorporating SSRE in to future SW-CRTs.

The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a risk a trial may be under- or overpowered. For many study designs, this problem has been addressed by considering adaptive design methodology that allows for the re-estimation of the required sample size during a trial. Here, we propose and compare several approaches for this in multitreatment crossover trials. Specifically, regulators favor reestimation procedures to maintain the blinding of the treatment allocations. We therefore develop blinded estimators for the within and between person variances, following simple or block randomization. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for period, the proposed estimators following block randomization are unbiased. We further provide a formula for the bias of the estimators following simple randomization. The performance of these procedures, along with that of an unblinded approach, is then examined utilizing three motivating examples, including one based on a recently completed four-treatment four-period crossover trial. Simulation results show that the performance of the proposed blinded procedures is in many cases similar to that of the unblinded approach, and thus they are an attractive alternative.

In many phase II trials in solid tumours, patients are assessed using endpoints based on the Response Evaluation Criteria in Solid Tumours (RECIST) scale. Often, analyses are based on the response rate. This is the proportion of patients who have an observed tumour shrinkage above a predefined level and no new tumour lesions. The augmented binary method has been proposed to improve the precision of the estimator of the response rate. The method involves modelling the tumour shrinkage to avoid dichotomising it. However, in many trials the best observed response is used as the primary outcome. In such trials, patients are followed until progression, and their best observed RECIST outcome is used as the primary endpoint. In this paper, we propose a method that extends the augmented binary method so that it can be used when the outcome is best observed response. We show through simulated data and data from a real phase II cancer trial that this method improves power in both single-arm and randomised trials. The average gain in power compared to the traditional analysis is equivalent to approximately a 35% increase in sample size. A modified version of the method is proposed to reduce the computational effort required. We show this modified method maintains much of the efficiency advantages.

In drug development, there is often uncertainty about the most promising among a set of different treatments. Multi-arm multi-stage (MAMS) trials provide large gains in efficiency over separate randomised trials of each treatment. They allow a shared control group, dropping of ineffective treatments before the end of the trial and stopping the trial early if sufficient evidence of a treatment being superior to control is found. In this paper, we discuss optimal design of MAMS trials. An optimal design has the required type I error rate and power but minimises the expected sample size at some set of treatment effects. Finding an optimal design requires searching over stopping boundaries and sample size, potentially a large number of parameters. We propose a method that combines quick evaluation of specific designs and an efficient stochastic search to find the optimal design parameters. We compare various potential designs motivated by the design of a phase II MAMS trial. We also consider allocating more patients to the control group, as has been carried out in real MAMS studies. We show that the optimal allocation to the control group, although greater than a 1:1 ratio, is smaller than previously advocated and that the gain in efficiency is generally small.

Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2-), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2- invasive lobular carcinoma (ILC). Dynamic 13C MRSI was performed following injection of hyperpolarized [1-13C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes 13C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized 13C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume (R = 0.903, P = 0.005) and MCT 1 (R = 0.85, P = 0.032) and HIF1α expression (R = 0.83, P = 0.043). Imaging of hyperpolarized [1-13C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia.

In many phase II trials in solid tumours, patients are assessed using endpoints based on the Response Evaluation Criteria in Solid Tumours (RECIST) scale. Often, analyses are based on the response rate. This is the proportion of patients who have an observed tumour shrinkage above a predefined level and no new tumour lesions. The augmented binary method has been proposed to improve the precision of the estimator of the response rate. The method involves modelling the tumour shrinkage to avoid dichotomising it. However, in many trials the best observed response is used as the primary outcome. In such trials, patients are followed until progression, and their best observed RECIST outcome is used as the primary endpoint. In this paper, we propose a method that extends the augmented binary method so that it can be used when the outcome is best observed response. We show through simulated data and data from a real phase II cancer trial that this method improves power in both single-arm and randomised trials. The average gain in power compared to the traditional analysis is equivalent to approximately a 35% increase in sample size. A modified version of the method is proposed to reduce the computational effort required. We show this modified method maintains much of the efficiency advantages.

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

Background Adaptive designs offer great promise in improving the efficiency and patient-benefit of clinical trials. An important barrier to further increased use is a lack of understanding about which additional resources are required to conduct a high-quality adaptive clinical trial, compared to a traditional fixed design. The Costing Adaptive Trials (CAT) project investigated which additional resources may be required to support adaptive trials. Methods We conducted a mock costing exercise amongst seven Clinical Trials Units (CTUs) in the UK. Five scenarios were developed, derived from funded clinical trials, where a non-adaptive version and an adaptive version were described. Each scenario represented a different type of adaptive design. CTU staff were asked to provide the costs and staff time they estimated would be needed to support the trial, categorised into specified areas (e.g. statistics, data management, trial management). This was calculated separately for the non-adaptive and adaptive version of the trial, allowing paired comparisons. Interviews with 10 CTU staff who had completed the costing exercise were conducted by qualitative researchers to explore reasons for similarities and differences. Results Estimated resources associated with conducting an adaptive trial were always (moderately) higher than for the non-adaptive equivalent. The median increase was between 2 and 4% for all scenarios, except for sample size re-estimation which was 26.5% (as the adaptive design could lead to a lengthened study period). The highest increase was for statistical staff, with lower increases for data management and trial management staff. The percentage increase in resources varied across different CTUs. The interviews identified possible explanations for differences, including (1) experience in adaptive trials, (2) the complexity of the non-adaptive and adaptive design, and (3) the extent of non-trial specific core infrastructure funding the CTU had. Conclusions This work sheds light on additional resources required to adequately support a high-quality adaptive trial. The percentage increase in costs for supporting an adaptive trial was generally modest and should not be a barrier to adaptive designs being cost-effective to use in practice. Informed by the results of this research, guidance for investigators and funders will be developed on appropriately resourcing adaptive trials.

Composite endpoints that combine multiple outcomes on different scales are common in clinical trials, particularly in chronic conditions. In many of these cases, patients will have to cross a predefined responder threshold in each of the outcomes to be classed as a responder overall. One instance of this occurs in systemic lupus erythematosus, where the responder endpoint combines two continuous, one ordinal and one binary measure. The overall binary responder endpoint is typically analysed using logistic regression, resulting in a substantial loss of information. We propose a latent variable model for the systemic lupus erythematosus endpoint, which assumes that the discrete outcomes are manifestations of latent continuous measures and can proceed to jointly model the components of the composite. We perform a simulation study and find that the method offers large efficiency gains over the standard analysis, the magnitude of which is highly dependent on the components driving response. Bias is introduced when joint normality assumptions are not satisfied, which we correct for using a bootstrap procedure. The method is applied to the Phase IIb MUSE trial in patients with moderate to severe systemic lupus erythematosus. We show that it estimates the treatment effect 2.5 times more precisely, offering a 60% reduction in required sample size.

Summary Multi-arm trials are an efficient way of simultaneously testing several experimental treatments against a shared control group. As well as reducing the sample size required compared to running each trial separately, they have important administrative and logistical advantages. There has been debate over whether multi-arm trials should correct for the fact that multiple null hypotheses are tested within the same experiment. Previous opinions have ranged from no correction is required, to a stringent correction (controlling the probability of making at least one type I error) being needed, with regulators arguing the latter for confirmatory settings. In this article, we propose that controlling the false-discovery rate (FDR) is a suitable compromise, with an appealing interpretation in multi-arm clinical trials. We investigate the properties of the different correction methods in terms of the positive and negative predictive value (respectively how confident we are that a recommended treatment is effective and that a non-recommended treatment is ineffective). The number of arms and proportion of treatments that are truly effective is varied. Controlling the FDR provides good properties. It retains the high positive predictive value of FWER correction in situations where a low proportion of treatments is effective. It also has a good negative predictive value in situations where a high proportion of treatments is effective. In a multi-arm trial testing distinct treatment arms, we recommend that sponsors and trialists consider use of the FDR.

Mixed outcome endpoints that combine multiple continuous and discrete components to form co-primary, multiple primary or composite endpoints are often employed as primary outcome measures in clinical trials. There are many advantages to joint modelling the individual outcomes using a latent variable framework, however in order to make use of the model in practice we require techniques for sample size estimation. In this paper we show how the latent variable model can be applied to the three types of joint endpoints and propose appropriate hypotheses, power and sample size estimation methods for each. We illustrate the techniques using a numerical example based on the four dimensional endpoint in the MUSE trial and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required for the multiple primary endpoint is reduced from that required for the individual outcome with the largest effect size. We show that the analytical technique agrees with the empirical power from simulation studies. We further illustrate the reduction in required sample size that may be achieved in trials of mixed outcome composite endpoints through a simulation study and find that the sample size primarily depends on the components driving response and the correlation structure and much less so on the treatment effect structure in the individual endpoints., 36 pages, 8 figures, 7 tables

Adapting the final sample size of a trial to the evidence accruing during the trial is a natural way to address planning uncertainty. Designs with adaptive sample size need to account for their optional stopping to guarantee strict type-I error-rate control. A variety of different methods to maintain type-I error-rate control after unplanned changes of the initial sample size have been proposed in the literature. This makes interim analyses for the purpose of sample size recalculation feasible in a regulatory context. Since the sample size is usually determined via an argument based on the power of the trial, an interim analysis raises the question of how the final sample size should be determined conditional on the accrued information. Conditional power is a concept often put forward in this context. Since it depends on the unknown effect size, we take a strict estimation perspective and compare assumed conditional power, observed conditional power, and predictive power with respect to their properties as estimators of the unknown conditional power. We then demonstrate that pre-planning an interim analysis using methodology for unplanned interim analyses is ineffective and naturally leads to the concept of optimal two-stage designs. We conclude that unplanned design adaptations should only be conducted as reaction to trial-external new evidence, operational needs to violate the originally chosen design, or post hoc changes in the objective criterion. Finally, we show that commonly discussed sample size recalculation rules can lead to paradoxical outcomes and propose two alternative ways of reacting to newly emerging trial-external evidence.

Objective : Sequential Multiple Assignment Randomised Trial (SMART) designs allow multiple randomisations of participants; this allows assessment of stage-specific questions (individual randomisations) and adaptive interventions (i.e. treatment strategies). We assessed the quality of reporting of the information required to design SMART studies. Study design and setting : We systematically searched four databases (PubMed, Ovid, Web of Science and Scopus) for all trial reports, protocols, reviews, and methodological papers which mentioned SMART designs up to June 15, 2020. Results : Of the 157 selected records, 12 (7.64%) were trial reports, 24 (15.29%) were study protocols, 91 (58%) were methodological papers, and 30 (19.1%) were review papers. All these trials were powered using stage-specific aims. Only four (33.33%) of these trials reported parameters required for sample size calculations. A small number of the trials (16.67 %) were interested in determining the best embedded adaptive interventions. Most of the trials did not report information about multiple testing adjustment. Furthermore, most of records reported designs that were mainly focused on stage-specific aims. Conclusions : Some features of SMART designs are seldomly reported and/or used. Furthermore, studies using this design tend to not adequately report information about all the design parameters, limiting their transparency and interpretability.

Hyperpolarized 13C-MRI is an emerging tool for probing tissue metabolism by measuring 13C-label exchange between intravenously injected hyperpolarized [1–13C]pyruvate and endogenous tissue lactate. Here, we demonstrate that hyperpolarized 13C-MRI can be used to detect early response to neoadjuvant therapy in breast cancer. Seven patients underwent multiparametric 1H-MRI and hyperpolarized 13C-MRI before and 7–11 days after commencing treatment. An increase in the lactate-to-pyruvate ratio of approximately 20% identified three patients who, following 5–6 cycles of treatment, showed pathological complete response. This ratio correlated with gene expression of the pyruvate transporter MCT1 and lactate dehydrogenase A (LDHA), the enzyme catalyzing label exchange between pyruvate and lactate. Analysis of approximately 2,000 breast tumors showed that overexpression of LDHA and the hypoxia marker CAIX was associated with reduced relapse-free and overall survival. Hyperpolarized 13C-MRI represents a promising method for monitoring very early treatment response in breast cancer and has demonstrated prognostic potential. Significance: Hyperpolarized carbon-13 MRI allows response assessment in patients with breast cancer after 7–11 days of neoadjuvant chemotherapy and outperformed state-of-the-art and research quantitative proton MRI techniques.

Background Participants in clinical research studies often do not reflect the populations for which healthcare interventions are needed or will be used. Enhancing representation of under-served groups in clinical research is important to ensure that research findings are widely applicable. We describe a multicomponent workstream project to improve representation of under-served groups in clinical trials. Methods The project comprised three main strands: (1) a targeted scoping review of literature to identify previous work characterising under-served groups and barriers to inclusion, (2) surveys of professional stakeholders and participant representative groups involved in research delivery to refine these initial findings and identify examples of innovation and good practice and (3) a series of workshops bringing together key stakeholders from funding, design, delivery and participant groups to reach consensus on definitions, barriers and a strategic roadmap for future work. The work was commissioned by the UK National Institute for Health Research Clinical Research Network. Output from these strands was integrated by a steering committee to generate a series of goals, workstream plans and a strategic roadmap for future development work in this area. Results ‘Under-served groups’ was identified and agreed by the stakeholder group as the preferred term. Three-quarters of stakeholders felt that a clear definition of under-served groups did not currently exist; definition was challenging and context-specific, but exemplar groups (e.g. those with language barriers or mental illness) were identified as under-served. Barriers to successful inclusion of under-served groups could be clustered into communication between research teams and participant groups; how trials are designed and delivered, differing agendas of research teams and participant groups; and lack of trust in the research process. Four key goals for future work were identified: building long-term relationships with under-served groups, developing training resources to improve design and delivery of trials for under-served groups, developing infrastructure and systems to support this work and working with funders, regulators and other stakeholders to remove barriers to inclusion. Conclusions The work of the INCLUDE group over the next 12 months will build on these findings by generating resources customised for different under-served groups to improve the representativeness of trial populations.

Background Clinical trials and other studies commonly assess the effectiveness of an intervention through the use of responder-based endpoints. These classify patients based on whether they meet a number of criteria which often involve continuous variables categorised as being above or below a threshold. The proportion of patients who are responders is estimated and, where relevant, compared between groups. An alternative method called the augmented binary method keeps the definition of the endpoint the same but utilises information contained within the continuous component to increase the power considerably (equivalent to increasing the sample size by > 30%). In this article we summarise the method and investigate the variety of clinical conditions that use endpoints to which it could be applied. Methods We reviewed a database of core outcome sets (COSs) that covered physiological and mortality trial endpoints recommended for collection in clinical trials of different disorders. We identified responder-based endpoints where the augmented binary method would be useful for increasing power. Results Out of the 287 COSs reviewed, we identified 67 new clinical areas where endpoints were used that would be more efficiently analysed using the augmented binary method. Clinical areas that had particularly high numbers were rheumatology (11 clinical disorders identified), non-solid tumour oncology (10 identified), neurology (9 identified) and cardiovascular (8 identified). Conclusions The augmented binary method can potentially provide large benefits in a vast array of clinical areas. Further methodological development is needed to account for some types of endpoints.

Background: The development of a nurse-led approach to managing epilepsy in adults with an intellectual disability offers the potential of improved outcomes and lower costs of care. We undertook a cluster randomised trial to assess the impact on costs and outcomes of the provision of intellectual disability nurses working to a designated epilepsy nurse competency framework. Here, we report the impact of the intervention on costs. Method: Across the UK, 8 sites randomly allocated to the intervention recruited 184 participants, 9 sites allocated to treatment as usual recruited 128 participants. Cost and outcome data were collected mainly by telephone interview at baseline and after six months. Total costs at six months were compared from the perspective of health & social services, and society, with adjustments for pre-specified participant and cluster characteristics at baseline including costs. Missing data was imputed using Multiple Imputation. Uncertainty was quantified by bootstrapping. Results: The intervention was associated with lower per participant costs from a health & social services perspective of -£357 (2014/15 GBP) (95% CI -£986, £294) and from a societal perspective of -£631 (95% CI -£1,473, £181). Results were not sensitive to the exclusion of accommodation costs. Conclusions: Our findings suggest that the competency framework is unlikely to increase the cost of caring for people with epilepsy and intellectual disability and may reduce costs., This paper presents independent research commissioned by the National Institute for Health Research (NIHR) under its Health Technology Assessment Programme (Grant 10/104/16 PI Dr H Ring). During the time that this research was carried out, H Ring also received support from the NIHR Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East of England at Cambridgeshire and Peterborough NHS Foundation Trust.

It is often of interest to explore how dose affects the toxicity and efficacy properties of a novel treatment. In oncology, efficacy is often assessed through response, which is defined by a patient having no new tumour lesions and their tumour size shrinking by 30%. Usually response and toxicity are analysed as binary outcomes in early phase trials. Methods have been proposed to improve the efficiency of analysing response by utilising the continuous tumour size information instead of dichotomising it. However, these methods do not allow for toxicity or for different doses. Motivated by a phase II trial testing multiple doses of a treatment against placebo, we propose a latent variable model that can estimate the probability of response and no toxicity (or other related outcomes) for different doses. We assess the confidence interval coverage and efficiency properties of the method, compared to methods that do not use the continuous tumour size, in a simulation study and the real study. The coverage is close to nominal when model assumptions are met, although can be below nominal when the model is misspecified. Compared to methods that treat response as binary, the method has confidence intervals with 30–50% narrower widths. The method adds considerable efficiency but care must be taken that the model assumptions are reasonable.

BACKGROUND: Many recent Stroke trials fail to show a beneficial effect of the intervention late in the development. Currently a large number of new treatment options are being developed. Multi-arm multi-stage (MAMS) designs offer one potential strategy to avoid lengthy studies of treatments without beneficial effects while at the same time allowing evaluation of several novel treatments. In this paper we provide a review of what MAMS designs are and argue that they are of particular value for Stroke trials. We illustrate this benefit through a case study based on previous published trials of endovascular treatment for acute ischemic stroke. We show in this case study that MAMS trials provide additional power for the same sample size compared to alternative trial designs. This level of additional power depends on the recruitment length of the trial, with most efficiency gained when recruitment is relatively slow. We conclude with a discussion of additional considerations required when starting a MAMS trial. CONCLUSION: MAMS trial designs are potentially very useful for stroke trials due to their improved statistical power compared to the traditional approach., This work was supported in part by grants the National Institute for Health Research (NIHR-SRF-2015-08-001, TJ), the Medical Research Council (SLAH/210 JW).

Background Composite endpoints are recommended in rare diseases to increase power and/or to sufficiently capture complexity. Often, they are in the form of responder indices which contain a mixture of continuous and binary components. Analyses of these outcomes typically treat them as binary, thus only using the dichotomisations of continuous components. The augmented binary method offers a more efficient alternative and is therefore especially useful for rare diseases. Previous work has indicated the method may have poorer statistical properties when the sample size is small. Here we investigate small sample properties and implement small sample corrections. Methods We re-sample from a previous trial with sample sizes varying from 30 to 80. We apply the standard binary and augmented binary methods and determine the power, type I error rate, coverage and average confidence interval width for each of the estimators. We implement Firth’s adjustment for the binary component models and a small sample variance correction for the generalized estimating equations, applying the small sample adjusted methods to each sub-sample as before for comparison. Results For the log-odds treatment effect the power of the augmented binary method is 20-55% compared to 12-20% for the standard binary method. Both methods have approximately nominal type I error rates. The difference in response probabilities exhibit similar power but both unadjusted methods demonstrate type I error rates of 6–8%. The small sample corrected methods have approximately nominal type I error rates. On both scales, the reduction in average confidence interval width when using the adjusted augmented binary method is 17–18%. This is equivalent to requiring a 32% smaller sample size to achieve the same statistical power. Conclusions The augmented binary method with small sample corrections provides a substantial improvement for rare disease trials using composite endpoints. We recommend the use of the method for the primary analysis in relevant rare disease trials. We emphasise that the method should be used alongside other efforts in improving the quality of evidence generated from rare disease trials rather than replace them. Electronic supplementary material The online version of this article (10.1186/s13023-018-0819-1) contains supplementary material, which is available to authorized users.

BackgroundPeople with an intellectual (learning) disability (ID) and epilepsy have an increased seizure frequency, higher frequencies of multiple antiepileptic drug (AED) use and side effects, higher treatment costs, higher mortality rates and more behavioural problems than the rest of the population with epilepsy. The introduction of nurse-led care may lead to improvements in outcome for those with an ID and epilepsy; however, this has not been tested in a definitive clinical trial.ObjectiveTo determine whether or not ID nurses, using a competency framework developed to optimise nurse management of epilepsy in people with an ID, can cost-effectively improve clinical and quality-of-life outcomes in the management of epilepsy compared with treatment as usual.DesignCluster-randomised two-arm trial.SettingCommunity-based secondary care delivered by members of community ID teams.ParticipantsParticipants were adults aged 18–65 years with an ID and epilepsy under the care of a community ID team and had had at least one seizure in the 6 months before the trial.InterventionsThe experimental intervention was the Learning Disability Epilepsy Specialist Nurse Competency Framework. This provides guidelines describing a structure and goals to support the delivery of epilepsy care and management by ID-trained nurses.Main outcome measuresThe primary outcome was the seizure severity scale from the Epilepsy and Learning Disabilities Quality of Life questionnaire. Measures of mood, behaviour, AED side effects and carer strain were also collected. A cost–utility analysis was undertaken along with a qualitative examination of carers’ views of participants’ epilepsy management.ResultsIn total, 312 individuals were recruited into the study from 17 research clusters. Using an intention-to-treat analysis controlling for baseline individual-level and cluster-level variables there was no significant difference in seizure severity score between the two arms. Altogether, 238 complete cases were included in the non-imputed primary analysis. Analyses of the secondary outcomes revealed no significant differences between arms. A planned subgroup analysis identified a significant interaction between treatment arm and level of ID. There was a suggestion in those with mild to moderate ID that the competency framework may be associated with a small reduction in concerns over seizure severity (standard error 2.005, 95% confidence interval –0.554 to 7.307;p = 0.092). However, neither subgroup showed a significant intervention effect individually. Family members’ perceptions of nurses’ management depended on the professional status of the nurses, regardless of trial arm. Economic analysis suggested that the competency framework intervention was likely to be cost-effective, primarily because of a reduction in the costs of supporting participants compared with treatment as usual.LimitationsThe intervention could not be delivered blinded. Treatment as usual varied widely between the research sites.ConclusionsOverall, for adults with an ID and epilepsy, the framework conferred no clinical benefit compared with usual treatment. The economic analysis suggested that there may be a role for the framework in enhancing the cost-effectiveness of support for people with epilepsy and an ID. Future research could explore the specific value of the competency framework for those with a mild to moderate ID and the potential for greater long-term benefits arising from the continuing professional development element of the framework.Trial registrationCurrent Controlled Trials ISRCTN96895428.FundingThis trial was funded by the NIHR Health Technology Assessment programme and will be published in full inHealth Technology Assessment; Vol. 22, No. 10. See the NIHR Journals Library website for further project information.

Background/Aims: The stepped-wedge cluster randomised trial design has received substantial attention in recent years. Although various extensions to the original design have been proposed, no guidance is available on the design of stepped-wedge cluster randomised trials with interim analyses. In an individually randomised trial setting, group sequential methods can provide notable efficiency gains and ethical benefits. We address this by discussing how established group sequential methodology can be adapted for stepped-wedge designs. Methods: Utilising the error spending approach to group sequential trial design, we detail the assumptions required for the determination of stepped-wedge cluster randomised trials with interim analyses. We consider early stopping for efficacy, futility, or efficacy and futility. We describe first how this can be done for any specified linear mixed model for data analysis. We then focus on one particular commonly utilised model and, using a recently completed stepped-wedge cluster randomised trial, compare the performance of several designs with interim analyses to the classical stepped-wedge design. Finally, the performance of a quantile substitution procedure for dealing with the case of unknown variance is explored. Results: We demonstrate that the incorporation of early stopping in stepped-wedge cluster randomised trial designs could reduce the expected sample size under the null and alternative hypotheses by up to 31% and 22%, respectively, with no cost to the trial’s type-I and type-II error rates. The use of restricted error maximum likelihood estimation was found to be more important than quantile substitution for controlling the type-I error rate. Conclusion: The addition of interim analyses into stepped-wedge cluster randomised trials could help guard against time-consuming trials conducted on poor performing treatments and also help expedite the implementation of efficacious treatments. In future, trialists should consider incorporating early stopping of some kind into stepped-wedge cluster randomised trials according to the needs of the particular trial.

Objective To prospectively investigate the longitudinal effect of ejaculatory abstinence on MRI-measured seminal vesicle (SV) volume and whole-prostate ADC over consecutive days. Methods 15 healthy male volunteers (mean 35.9 years, range 27–53) underwent 3-T MRI at baseline and 1, 2 and 3 days post-ejaculation. Prostate and SV volumes were derived by volume segmentation and whole-gland apparent diffusion coefficient (ADC) values calculated. A mixed-effects linear regression compared ADC values and prostate/seminal vesicle volumes in each volunteer between studies in a pairwise manner. Results All subjects completed the four MRIs. Mean prostate volume was 22.45 cm3 (range 13.04–31.21 cm3), with no change between the four studies (p = 0.89–0.99). 13/15 subjects showed SV volume reduction from baseline to day 1, with group-mean decreasing from 6.45 to 4.80 cm3 (−25.6%, p

Multi-arm multi-stage trials can improve the efficiency of the drug development process when multiple new treatments are available for testing. A group-sequential approach can be used in order to design multi-arm multi-stage trials, using an extension to Dunnett’s multiple-testing procedure. The actual sample size used in such a trial is a random variable that has high variability. This can cause problems when applying for funding as the cost will also be generally highly variable. This motivates a type of design that provides the efficiency advantages of a group-sequential multi-arm multi-stage design, but has a fixed sample size. One such design is the two-stage drop-the-losers design, in which a number of experimental treatments, and a control treatment, are assessed at a prescheduled interim analysis. The best-performing experimental treatment and the control treatment then continue to a second stage. In this paper, we discuss extending this design to have more than two stages, which is shown to considerably reduce the sample size required. We also compare the resulting sample size requirements to the sample size distribution of analogous group-sequential multi-arm multi-stage designs. The sample size required for a multi-stage drop-the-losers design is usually higher than, but close to, the median sample size of a group-sequential multi-arm multi-stage trial. In many practical scenarios, the disadvantage of a slight loss in average efficiency would be overcome by the huge advantage of a fixed sample size. We assess the impact of delay between recruitment and assessment as well as unknown variance on the drop-the-losers designs.

Background: Mesothelioma is an incurable cancer originating from the mesothelial cells that line the pleural, peritoneal and pericardial cavities. These cells synthesise large quantities of surface glycoproteins, rendering them dependent upon efficient endoplasmic reticulum (ER) function. When faced with elevated levels of secretory protein load, cells are said to experience ER stress, which has been implicated in the pathogenesis of many human diseases including cancer. Method: We set out to measure markers of ER stress in malignant mesothelioma and to determine whether ER stress signalling correlates with clinical parameters. Results: We observed that expression of the ER stress-responsive transcription factor C/EBP homologous protein (CHOP) correlated with patient survival and remained an independent prognostic variable in pairwise comparisons with all clinical variables tested. The most parsimonious multivariate model in our study comprised only performance status and CHOP staining. In contrast, expression of the ER stress-responsive phosphatase growth arrest and DNA damage 34 (GADD34) correlated with the degree of mesothelial differentiation, being lost progressively in biphasic and sarcomatoid mesotheliomas. Conclusion: Our findings suggest that staining for CHOP provides prognostic information that may be useful in the stratification of patients with mesothelioma. Staining for GADD34 may prove useful in classification of mesothelioma histopathology.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

Background In adults with intellectual disability (ID) and epilepsy there are suggestions that improvements in management may follow introduction of epilepsy nurse-led care. However, this has not been tested in a definitive clinical trial and results cannot be generalised from general population studies as epilepsy tends to be more severe and to involve additional clinical comorbidities in adults with ID. This trial investigates whether nurses with expertise in epilepsy and ID, working proactively to a clinically defined role, can improve clinical and quality of life outcomes in the management of epilepsy within this population, compared to treatment as usual. The trial also aims to establish whether any perceived benefits represent good value for money. Methods/design The EpAID clinical trial is a two-arm cluster randomised controlled trial of nurse-led epilepsy management versus treatment as usual. This trial aims to obtain follow-up data from 320 participants with ID and drug-resistant epilepsy. Participants are randomly assigned either to a ‘treatment as usual’ control or a ‘defined epilepsy nurse role’ active arm, according to the cluster site at which they are treated. The active intervention utilises the recently developed Learning Disability Epilepsy Specialist Nurse Competency Framework for adults with ID. Participants undergo 4 weeks of baseline data collection, followed by a minimum of 20 weeks intervention (novel treatment or treatment as usual), followed by 4 weeks of follow-up data collection. The primary outcome is seizure severity, including associated injuries and the level of distress manifest by the patient in the preceding 4 weeks. Secondary outcomes include cost-utility analysis, carer strain, seizure frequency and side effects. Descriptive measures include demographic and clinical descriptors of participants and clinical services in which they receive their epilepsy management. Qualitative study of clinical interactions and semi-structured interviews with clinicians and participants’ carers are also undertaken. Discussion The EpAID clinical trial is the first cluster randomised controlled trial to test possible benefits of a nurse-led intervention in adults with epilepsy and ID. This research will have important implications for ID and epilepsy services. The challenges of undertaking such a trial in this population, and the approaches to meeting these are discussed. Trial registration International Standard Randomised Controlled Trial Number: ISRCTN96895428 version 1.1. Registered on 26 March 2013. Electronic supplementary material The online version of this article (doi:10.1186/s13063-016-1429-7) contains supplementary material, which is available to authorized users.

The Gittins index provides a well established, computationally attractive, optimal solution to a class of resource allocation problems known collectively as the multi-arm bandit problem. Its development was originally motivated by the problem of optimal patient allocation in multi-arm clinical trials. However, it has never been used in practice, possibly for the following reasons: (1) it is fully sequential, i.e., the endpoint must be observable soon after treating a patient, reducing the medical settings to which it is applicable; (2) it is completely deterministic and thus removes randomization from the trial, which would naturally protect against various sources of bias. We propose a novel implementation of the Gittins index rule that overcomes these difficulties, trading off a small deviation from optimality for a fully randomized, adaptive group allocation procedure which offers substantial improvements in terms of patient benefit, especially relevant for small populations. We report the operating characteristics of our approach compared to existing methods of adaptive randomization using a recently published trial as motivation.

BACKGROUND: The detection of occult bone metastases is a key factor in determining the management of patients with renal cell carcinoma (RCC), especially when curative surgery is considered. This prospective study assessed the sensitivity of (18)F-labelled sodium fluoride in conjunction with positron emission tomography/computed tomography ((18)F-NaF PET/CT) for detecting RCC bone metastases, compared with conventional imaging by bone scintigraphy or CT. PATIENTS AND METHODS: An adaptive two-stage trial design was utilized, which was stopped after the first stage due to statistical efficacy. Ten patients with stage IV RCC and bone metastases were imaged with (18)F-NaF PET/CT and (99m)Tc-labelled methylene diphosphonate ((99m)Tc-MDP) bone scintigraphy including pelvic single photon emission computed tomography (SPECT). Images were reported independently by experienced radiologists and nuclear medicine physicians using a 5-point scoring system. RESULTS: Seventy-seven lesions were diagnosed as malignant: 100% were identified by (18)F-NaF PET/CT, 46% by CT and 29% by bone scintigraphy/SPECT. Standard-of-care imaging with CT and bone scintigraphy identified 65% of the metastases reported by (18)F-NaF PET/CT. On an individual patient basis, (18)F-NaF PET/CT detected more RCC metastases than (99m)Tc-MDP bone scintigraphy/SPECT or CT alone (P = 0.007). The metabolic volumes, mean and maximum standardized uptake values (SUV mean and SUV max) of the malignant lesions were significantly greater than those of the benign lesions (P < 0.001). CONCLUSIONS: (18)F-NaF PET/CT is significantly more sensitive at detecting RCC skeletal metastases than conventional bone scintigraphy or CT. The detection of occult bone metastases could greatly alter patient management, particularly in the context when standard-of-care imaging is negative for skeletal metastases.

Primary spontaneous pneumothorax (PSP) occurs in apparently healthy young people with an incidence of 12.5 cases per 100 000 per year [1]. Attempts to develop standardised care guidelines for this condition have been severely hampered by a lack of high-quality clinical research into this condition. The American College of Chest Physicians (CHEST) concluded in 2001 that “insufficient data exist…to develop an evidence-based document” and so produced a consensus statement based on expert opinion [2]. Similarly, the British Thoracic Society (BTS) 2010 guidelines are based predominantly on nonanalytical studies and expert opinion [3]. In both documents, the size of the presenting PSP is used to determine initial treatment. A “small” PSP without respiratory compromise is thought not to require intervention, while a “large” PSP has typically been treated either by aspiration or intrapleural drainage. Implicit in these definitions is the belief that large pneumothoraces will not respond well to conservative management. Remarkably, no consensus regarding the definition of PSP severity exists, with CHEST and the BTS each using different arbitrary measurements of the presentation chest radiograph. When these measurements were compared directly to one another, they showed poor correlation [4]. This lack of a clinically useful radiological biomarker for pneumothoraces requiring intervention hinders the development of evidence-based care of this condition. We wished to determine whether the BTS definition of large pneumothorax (>2 cm at the hilum) or CHEST definition (>3 cm from apex to cupola) better predicts the requirement for intercostal chest drain (ICD) insertion., Hilar rather than apical interpleural distance more accurately predicts need for intercostal chest drain insertion http://ow.ly/JvKFY

BACKGROUND: Response to treatments is highly heterogeneous in cancer. Increased availability of biomarkers and targeted treatments has led to the need for trial designs that efficiently test new treatments in biomarker-stratified patient subgroups. METHODS: We propose a novel Bayesian adaptive randomisation (BAR) design for use in multi-arm phase II trials where biomarkers exist that are potentially predictive of a linked treatment's effect. The design is motivated in part by two phase II trials that are currently in development. The design starts by randomising patients to the control treatment or to experimental treatments that the biomarker profile suggests should be active. At interim analyses, data from treated patients are used to update the allocation probabilities. If the linked treatments are effective, the allocation remains high; if ineffective, the allocation changes over the course of the trial to unlinked treatments that are more effective. RESULTS: Our proposed design has high power to detect treatment effects if the pairings of treatment with biomarker are correct, but also performs well when alternative pairings are true. The design is consistently more powerful than parallel-groups stratified trials. CONCLUSIONS: This BAR design is a powerful approach to use when there are pairings of biomarkers with treatments available for testing simultaneously.

Background Genome-wide association studies (GWAS) are a widely used study design for detecting genetic causes of complex diseases. Current studies provide good coverage of common causal SNPs, but not rare ones. A popular method to detect rare causal variants is haplotype testing. A disadvantage of this approach is that many parameters are estimated simultaneously, which can mean a loss of power and slower fitting to large datasets. Haplotype testing effectively tests both the allele frequencies and the linkage disequilibrium (LD) structure of the data. LD has previously been shown to be mostly attributable to LD between adjacent SNPs. We propose a generalised linear model (GLM) which models the effects of each SNP in a region as well as the statistical interactions between adjacent pairs. This is compared to two other commonly used multimarker GLMs: one with a main-effect parameter for each SNP; one with a parameter for each haplotype. Results We show the haplotype model has higher power for rare untyped causal SNPs, the main-effects model has higher power for common untyped causal SNPs, and the proposed model generally has power in between the two others. We show that the relative power of the three methods is dependent on the number of marker haplotypes the causal allele is present on, which depends on the age of the mutation. Except in the case of a common causal variant in high LD with markers, all three multimarker models are superior in power to single-SNP tests. Including the adjacent statistical interactions results in lower inflation in test statistics when a realistic level of population stratification is present in a dataset. Using the multimarker models, we analyse data from the Molecular Genetics of Schizophrenia study. The multimarker models find potential associations that are not found by single-SNP tests. However, multimarker models also require stricter control of data quality since biases can have a larger inflationary effect on multimarker test statistics than on single-SNP test statistics. Conclusions Analysing a GWAS with multimarker models can yield candidate regions which may contain rare untyped causal variants. This is useful for increasing prior odds of association in future whole-genome sequence analyses.

Background The stepped wedge (SW) cluster randomized controlled trial (CRCT) design is being used with increasing frequency. However, there is limited published research on the quality of reporting of SW-CRCTs. We address this issue by conducting a literature review. Methods Medline, Ovid, Web of Knowledge, the Cochrane Library, PsycINFO, the ISRCTN registry, and ClinicalTrials.gov were searched to identify investigations employing the SW-CRCT design up to February 2015. For each included completed study, information was extracted on a selection of criteria, based on the CONSORT extension to CRCTs, to assess the quality of reporting. Results A total of 123 studies were included in our review, of which 39 were completed trial reports. The standard of reporting of SW-CRCTs varied in quality. The percentage of trials reporting each criterion varied to as low as 15.4%, with a median of 66.7%. Conclusions There is much room for improvement in the quality of reporting of SW-CRCTs. This is consistent with recent findings for CRCTs. A CONSORT extension for SW-CRCTs is warranted to standardize the reporting of SW-CRCTs. Electronic supplementary material The online version of this article (doi:10.1186/s13063-017-1783-0) contains supplementary material, which is available to authorized users.